Test #DataRobot

Yiqian Jin [email protected] Apr.26

Topic: Breast Cancer Prediction

Data: 683 observations with 10 features: "Cl.thickness" "Cell.size" "Cell.shape" "Marg.adhesion" "Epith.c.size" "Bare.nuclei" "Bl.cromatin" "Normal.nucleoli" "Mitoses" "Class"

and two classification results: "benign" "malignant"

The .R script shows the whole process of this model building:

1. Input data and randomly seperate data into dataset into 90% training set(615 obs.) and 10% testing set(68 obs.).

2. Build the model in API using training data.

3. Perform prediction on testing data and estimate the model.

Thanks!

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